Alsvior / AI practice

AI systems that turn knowledge into completed work.

We design and integrate workflows that coordinate tasks, check evidence and make exceptions visible. Start with a concrete customer onboarding example, then discuss where a bounded pilot could help your team.

Explore the demonstration

Where work gets stuck

Onboarding delayed by handovers

Knowledge split across documents

Repetitive document processing

Disconnected tools and owners

One workflow, three paths

See the handovers and the decision.

Advance the simulation, introduce a conflicting fact or let delivery time out. Every path ends in either a tested outcome or an owned exception.

ALSVIOR AI LAB / SIMULATION 01

Customer onboarding hive

Based on patterns from real customer cases. People, records, dialogue and outcomes here use sample data in a scripted simulation.

CASE-INSPIRED SIMULATION
No live customer data or systems

Static walkthrough: Maya receives an onboarding request, assigns customer success, identity, delivery and knowledge tasks, then asks Rae to test completion. A conflicting role source blocks access; a delivery timeout requires reconciliation before retry. Customer operations owns either exception. All records use sample data.

Change the case

CASE ONB-0240 / 24 EVENTSREADY

COORDINATION MAP00 KNOWLEDGE NODES

AgentKnowledge recordActive messageDrag nodes · drag background · scroll to zoom

HIVE / CASE CONVERSATION

PAUSED

Ready to inspect the work.

Play the case or advance one message at a time. Every exchange below is scripted from sample records.

SHARED KNOWLEDGE / LIVE CASE RECORD

Watch knowledge take shape.

Each node is created at a specific message. Select a record to see what it says, where it came from, who may use it and whether it is approved.

00records created

0 sources 0 candidates 0 approved / results 0 conflicts / exceptions

No records yet. Play the case to watch the first source appear.

No knowledge has been created yet.

Source records, candidate claims, validation results and exceptions will appear here as the conversation progresses.

All names, documents, permissions, dates and outcomes shown here are sample data. No source content or customer identity is exposed.

Agent roster

Presence follows assigned work

MayaOperations Lead Agentonline

Role in case: Orchestrator
Task: Sequence work and own the case
Permitted action: Assign, pause and route; no customer action
Inputs: Task contract and evidence
Result: Accepted outcome or owned exception

NoraCustomer Success Agentoffline

Role in case: Intake
Task: Validate the request
Permitted action: Read order and user list
Inputs: Order v3, user list v2
Result: Versioned intake record

AdaKnowledge Analyst Agentoffline

Role in case: Knowledge
Task: Check reusable facts
Permitted action: Propose or withhold scoped claim
Inputs: Source, scope, permission, expiry
Result: Approved or conflicted record

KaiIdentity Engineer Agentoffline

Role in case: Access
Task: Prepare access
Permitted action: Propose; no live grant
Inputs: Users and approved role claim
Result: Proposal and simulated test

LeoDelivery Coordinator Agentoffline

Role in case: Delivery
Task: Prepare handover
Permitted action: Draft; no external send
Inputs: Order, contact and scope
Result: Reviewed welcome draft

RaeQuality Assurance Agentoffline

Role in case: Verifier
Task: Test business completion
Permitted action: Accept or assign exception
Inputs: Branch outputs and evidence
Result: Case decision and evidence trail

Business outcome

STANDARD PATH

Pending acceptance

Agent outputs are intermediate. Completion requires evidence, acceptance tests and an accountable owner.

Keyboard: focus this panel and use ← / → for messages. Focus the graph and use arrow keys to pan, + / − to zoom, and Escape to reset. Drag nodes to stretch and release them; drag empty space to pan. Reduced-motion settings stop the ambient spring movement. All dialogue and records use sample data.

How the system is designed

Orchestration

Typed tasks, dependencies and an accountable owner.

Shared state

A durable record of progress, evidence and decisions.

Knowledge

Source, version, scope, permission and expiry travel with each claim.

Authority

Tools enforce permission outside model instructions.

Evaluation

Acceptance tests distinguish output from a completed business result.

Recovery

Failed attempts are reconciled before a safe retry; exceptions have an owner.

Retrieving a knowledge record does not update model weights. Summaries retain the source record’s access restrictions.

Proposed engagement routes

Start with a defined workflow.

Scope and capacity are agreed before an engagement begins.

Workflow assessment

What it produces

Process map, constraints, baseline and pilot recommendation.

What we need from you

A workflow owner, sample cases, systems and current measures.

Single-workflow pilot

What it produces

Bounded integration, evaluation cases, completion criteria, exception route and handover.

What we need from you

Approved scope, test access, representative cases and decision makers.

Ongoing AI operations

What it produces

A proposed operating model for monitoring, evaluation after changes, cost review and incident ownership.

What we need from you

Agreed ownership, access, operating targets and service boundaries.

Explore the working materials.

The AI Lab contains illustrative demonstrations and reusable templates for teams evaluating a specific workflow.

Visit AI Lab →

Discuss one workflow

Bring the current steps, a frequent exception and what completion means to your team. We can use them to frame an assessment or pilot.